Authors : Abdullayev Bilol

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/m66fe2t6

Scribd : https://tinyurl.com/btz5cc4r

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT1264

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This research paper discusses the current security situation in Uzbekistan and emphasizes the abovementioned problems connected with increasingly operating network attacks. Using the example of Estonia, it analyzes in general terms how AI algorithms, in particular artificial neural networks, may both worsen and improve state cybersecurity. The study is intended to serve two main purposes: assessing the current state of cybersecurity in Uzbekistan for common threats and vulnerabilities, as well as testing AI techniques to protect against these threats. AI 'is the only solution which can fight different cybersecurity threats effectively', the research notes, highlighting that AI is essential to increase Uzbekistan's capability to absorb cyberattacks and protect critical infrastructures and ensure quality of digital resources.

Keywords : Artificial Intelligence (AI), Cyber Threat Detection, Cybersecurity, Cyber Attacks, AI-Based Strategies.

References :

1. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
2. Qayumova, G. (2024). The Role of IT and Computer Science Education in Uzbekistan: The Impact of UNESCO Initiatives. European Journal of Applied Science, Engineering and Technology, 2(5), 16-19. DOI: 10.59324/ejaset.2024.2(5).02.
3. Chio, C., & Freeman, D. (2018). Machine Learning and Security: Protecting Systems with Data and Algorithms. O'Reilly Media.
4. Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.
5. Papernot, N., et al. (2016). The limitations of deep learning in adversarial settings. 2016 IEEE European Symposium on Security and Privacy (EuroS&P), 372-387.
6. Lippmann, R., Haines, J. W., Fried, D., Korba, J., & Das, K. (2000). The 1999 DARPA off-line intrusion detection evaluation. Computer Networks, 34(4), 579-595.
7. Mohamad Noor, M. B., Hassan, W. H., & Norwawi, N. (2020). Machine learning algorithms for anomaly detection: a systematic review. Journal of Network and Computer Applications, 166, 102673.
8. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE Symposium on Security and Privacy (pp. 305-316). IEEE.
9. Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31. doi:10.1016/j.jnca.2015.11.016
10. Bostrom, N., & Yudkowsky, E. (2014). The Ethics of Artificial Intelligence. In K. Frankish & W. M. Ramsey (Eds.), The Cambridge Handbook of Artificial Intelligence (pp. 316-334). Cambridge University Press.
11. Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., ... & Amodei, D. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. arXiv preprint arXiv:1802.07228.
12. Nguyen, T. T., Yang, D., Zhang, Y., & Rosé, C. P. (2018). Argument mining for improving the automated scoring of persuasive essays. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
13. Shaukat, K., & Ribeiro, E. (2018). Cyber threat detection using machine learning techniques: A performance evaluation perspective. In 2018 International Conference on Computer and Applications (ICCA) (pp. 33-40). IEEE.